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Computational methods for surgeries and therapies planning, evaluation, and follow-up through medical images.

Abstract

Medical imaging has been used extensively as a basis and support for diagnosis, and treatment is traditionally discussed and defined from diagnosis. Although the image analysis is recognized as critical to the diagnostic process, the research dedicated to the systematization and integration of medical imaging to support planning, monitoring, monitoring and evaluation of surgical and therapeutic interventions is emerging as a promising field. This proposal aims to create a collection of computational methods for image processing, i.e., pre-processing, segmentation, registration, and analysis of medical images aimed to image-guided therapies (IGT). It is expected that this project contributes, collaborates, and extends with new methods to 3DSlicer platform created in the Surgical Planning Laboratory (SPL) at Harvard Medical School (HMS) and developed and maintained by a worldwide community. Such methods and computational tools to be developed in this project will be validated in real situations, with appropriate casuistic and case numbers, allowing publications in journals and selected specialized events, and some methods which have appropriate characteristics and results will be chosen for submission of patents. In summary, this project is focused on creating new methodologies in computing three-dimensional medical images to support planning, monitoring, monitoring, and evaluation of surgeries and therapies within the collaborative platform for the treatment of volumetric and augmented reality 3D Slicer. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
AZIMBAGIRAD, MEHRAN; SIMOZO, FABRICIO H.; SENRA FILHO, ANTONIO C. S.; MURTA JUNIOR, LUIZ O. Tsallis-Entropy Segmentation through MRF and Alzheimer anatomic reference for Brain Magnetic Resonance Parcellation. MAGNETIC RESONANCE IMAGING, v. 65, p. 136-145, JAN 2020. Web of Science Citations: 0.
SENRA FILHO, ANTONIO CARLOS DA S.; SIMOZO, FABRICIO HENRIQUE; DOS SANTOS, ANTONIO CARLOS; MURTA JUNIOR, LUIZ OTAVIO. Multiple Sclerosis multimodal lesion simulation tool (MS-MIST). BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, v. 5, n. 3 APR 2019. Web of Science Citations: 0.

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